CN117068190A - Dual-motor system fault diagnosis method and device and electric forklift - Google Patents
Dual-motor system fault diagnosis method and device and electric forklift Download PDFInfo
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- CN117068190A CN117068190A CN202311236175.8A CN202311236175A CN117068190A CN 117068190 A CN117068190 A CN 117068190A CN 202311236175 A CN202311236175 A CN 202311236175A CN 117068190 A CN117068190 A CN 117068190A
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 58
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- 238000004458 analytical method Methods 0.000 claims abstract description 18
- 230000003750 conditioning effect Effects 0.000 claims abstract description 13
- 238000004804 winding Methods 0.000 claims description 33
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- 238000000354 decomposition reaction Methods 0.000 claims description 6
- 238000005461 lubrication Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 claims description 4
- 238000013021 overheating Methods 0.000 claims description 4
- 230000032683 aging Effects 0.000 claims description 3
- 230000002431 foraging effect Effects 0.000 claims description 3
- 230000016507 interphase Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
- B60W50/0205—Diagnosing or detecting failures; Failure detection models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0061—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electrical machines
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2200/00—Type of vehicles
- B60L2200/40—Working vehicles
- B60L2200/42—Fork lift trucks
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2300/00—Indexing codes relating to the type of vehicle
- B60W2300/12—Trucks; Load vehicles
- B60W2300/121—Fork lift trucks, Clarks
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- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Human Computer Interaction (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Control Of Electric Motors In General (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The application discloses a fault diagnosis method and device for a double-motor system and an electric forklift, and belongs to the technical field of fault diagnosis of motor systems, wherein the method comprises the following steps: s1, data hardware acquisition; s2, conditioning the currently acquired original fault signals; s3, inputting system setting to perform fault diagnosis and analysis. The device is applied to the fault diagnosis method of the double-motor system. An electric forklift has the device and/or a fault diagnosis method of a double-motor system is applied. The application has the advantages compared with the prior art that: the safety is improved, the downtime is reduced, the working efficiency is improved, the service life of equipment is prolonged, and the maintenance cost is reduced.
Description
Technical Field
The application relates to the technical field of motor system fault diagnosis, in particular to a double-motor system fault diagnosis method and device and an electric forklift.
Background
The driving motor is a power source for running of the electric forklift, the pump motor is a power source for the electric forklift during working, the driving motor and the pump motor are conversion components between electric energy and mechanical energy, and the performance of the motor system is related to all functions related to the movement of the electric forklift. When the electric forklift is used for a long time or runs at a high speed, the motor is internally provided with a motor system working temperature exceeding the rated temperature due to the factors such as overlarge bearing rotating speed or damage to a stator and a rotor, and the like, and the motor power, the rotating speed, the torque and other performances are abnormal along with overload and temperature rise of a motor or a motor controller, so that the motor faults can be diagnosed timely, the service life of the motor can be effectively prolonged, the accident rate can be reduced, and the safety of the whole forklift can be improved.
At present, the diagnosis of motor faults mainly depends on methods such as a physical model, expert knowledge and the like, and has the defects of available field Jing Shao, low diagnosis efficiency, poor generalization and the like, so that the faults of the motor cannot be effectively solved in time.
Disclosure of Invention
The application aims to solve the technical problems in the background art, and the first aspect provides a double-motor system fault diagnosis method which can effectively diagnose the cause of the double-motor system fault in time.
A second aspect provides an apparatus for use in a dual motor system fault diagnosis method.
A third aspect provides an electric forklift having an apparatus to which a dual motor system fault diagnosis method is applied, or to which a dual motor system fault diagnosis method is applied.
According to the first aspect, the present application provides the following technical solutions: a fault diagnosis method of a double-motor system comprises the following steps:
s1, data hardware acquisition;
s11, vibration signals of the driving motor and the pump motor in normal states and different fault states are acquired through the vibration sensor;
s12, performing fault simulation on common faults of the driving motor and the pump motor through fault measurement equipment to obtain electric signals of current, voltage, rotating speed and torque during normal operation and fault operation of the driving motor and the pump motor;
s2, conditioning the currently acquired original fault signals;
s21, according to the data of the input vibration signals, the operation states of the driving motor and the pump motor are identified through a signal processing method and an intelligent method, and the vibration signals are processed into time domain features of the vibration data;
s22, preprocessing a vibration signal into time domain characteristics of vibration data according to the running states of the driving motor and the pump motor which are identified by the signal processing method and the intelligent method;
s3, fault diagnosis and analysis are carried out;
s31, performing fault simulation on common faults of the driving motor and the pump motor, and acquiring data of current, voltage, rotating speed and torque during normal operation and fault operation of the driving motor and the pump motor;
s32, carrying out data processing on the vibration signals and the electric signals, importing the vibration signals and the electric signals into a system for fault diagnosis, preprocessing the acquired data through wavelet packet decomposition, extracting fault feature vectors, finally inputting the feature vectors into a BP neural network for training, providing data support for the identification and classification of the fault modes of the neural network, and carrying out the identification and judgment of the fault types of the driving motor and the pump motor;
s33, processing data of vibration signals and electric signals acquired when faults occur, and importing the vibration signals and the electric signals into a system to perform fault diagnosis, so that fault mode identification of the driving motor and the pump motor is realized.
In some embodiments, the vibration signals and electrical signals of the normal state and the fault simulation state of the drive motor and pump motor apparatus include:
dividing system faults into drive motor faults, drive motor controller faults, stator winding faults and bearing faults according to the types of the drive motor faults;
the system faults are divided into interphase short circuit, single-phase grounding, open-circuit faults, demagnetizing faults and IGBT faults according to the fault types of the pump motors.
In some embodiments, the fault signal conditioning obtains the eigenvectors corresponding to the current, the voltage, the torque and the rotating speed under other fault states through wavelet packet decomposition and reconstruction, and uses the eigenvectors as an input sample of the BP neural network to provide data support for the recognition and classification of the neural network fault modes.
In some embodiments, the fault diagnosis analysis algorithm comprises an optimized shoal algorithm, the optimized shoal algorithm steps being as follows:
step one, initializing a fish swarm, wherein the fish swarm comprises a population scale, a fish initial position, a fish foraging speed and the like;
step two, calculating the fitness value, the individual extremum, the population extremum and the like of each fish in the solution space according to the objective function;
step three, comparing the fitness value with the individual extremum for each fish, and when the fitness value is smaller than the individual extremum, replacing the individual extremum with the fitness value to update the individual extremum; comparing the fitness value with the population extremum, and when the fitness value is less than the population extremum, replacing the population extremum with the fitness value to update the population extremum;
step four, iteratively updating the position and the speed of the fish in the solution space;
and fifthly, calculating individual extremum and population extremum of the particles after iterative updating, comparing the individual extremum and population extremum with a termination condition, outputting an optimized optimal solution if the conditions are met, and repeating the steps of the steps II, III and IV if the conditions are not met until the output conditions are met or the set maximum iteration times are reached.
In some embodiments, device parameters are entered into the system settings for model analysis and user management, the system settings including possible failure causes and treatments as follows;
winding faults, which may be caused by winding wire breaks, poor contacts, short circuits, etc.; the processing method comprises checking and repairing winding connection, replacing winding wires, repairing or replacing windings; virtual ground faults, one phase of the motor windings being grounded, may be due to aging of winding insulation, insulation damage or external environment; the processing method comprises checking the insulation condition of the winding, repairing or replacing the winding insulation;
bearing faults, and problems such as noise, vibration and the like can be caused by abrasion, overheating or poor lubrication of a motor bearing; the processing method comprises the steps of checking the condition of the bearing, repairing or replacing the bearing, and ensuring good lubrication;
overload faults, overheat and damage of windings can be caused by long-time overload operation of the motor; the processing method comprises the steps of adjusting the load of the motor, and avoiding long-time overload operation;
vibration and imbalance, which may be due to motor rotating part imbalance or axial errors; the processing method comprises the steps of performing dynamic balance adjustment to ensure balance of rotating parts of the motor;
power supply problems, unstable power supply of the motor, and excessive high or low voltage can cause motor faults; the processing method comprises the steps of checking power supply and repairing power supply problems.
According to a second aspect, the present application provides a diagnostic apparatus, to which the fault diagnosis method for a dual-motor system provided in the first aspect is applied.
In some embodiments, the system comprises a data hardware acquisition module, a fault signal conditioning module, a fault diagnosis analysis module and a system setting module;
the data hardware acquisition module is used for acquiring data of different faults of the motor;
the fault signal conditioning module displays the acquired vibration signals to a data acquisition interface through a data acquisition industrial personal computer, a data acquisition card and LabVIEW software, and displays the vibration signals to a user in a graphical mode, wherein the vibration signals include but are not limited to time domain waveform diagrams and spectrograms;
the fault diagnosis analysis module can process the vibration signals and guide the vibration signals into the system to perform fault diagnosis, and the displayed results represent the running states of the driving motor and the pump motor of the corresponding electric fork truck, so that the whole fault diagnosis process of the fault motor is completed.
According to a third aspect, the present application provides an electric forklift, to which the dual-motor system fault diagnosis method mentioned in the first aspect is applied, and/or to which the diagnosis device provided in the second aspect is provided.
Compared with the prior art, the application has the beneficial effects that:
1. the safety is improved: the motor of the electric forklift is out of control or suddenly stops due to the failure, and potential danger can be prevented by detecting the failure, so that the safety of a workplace is ensured.
2. Reducing downtime: and the downtime can be reduced by timely detecting and repairing the motor faults, and the smooth progress of the working progress is ensured.
3. Increase equipment life: the motor faults can be found and solved in time, further damage can be prevented, the service life of the forklift is prolonged, and maintenance and replacement costs are reduced.
4. Work efficiency is improved: the failure of the motor of the electric forklift possibly causes low transportation efficiency, and the failure can be timely detected and repaired to ensure the normal operation of equipment, so that the working efficiency is improved.
5. The maintenance cost is reduced: through regular fault detection, problems can be found in advance, corresponding maintenance measures can be taken, further expansion of faults is avoided, and maintenance cost is reduced.
Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
FIG. 1 is a fault diagram of a dual motor system according to a first embodiment of the present application;
FIG. 2 is a flowchart of an optimization of a fault diagnosis and analysis algorithm according to a first embodiment of the present application;
fig. 3 is a flowchart of a motor system fault diagnosis method according to an embodiment of the present application.
Detailed Description
The following detailed description of the technical solutions of the present application will be given by way of the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limiting the technical solutions of the present application, and that the embodiments and technical features of the embodiments of the present application may be combined with each other without conflict.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Embodiment one:
the first embodiment of the application provides a fault diagnosis method of a double-motor system, which comprises data hardware acquisition, fault signal conditioning, fault diagnosis analysis and system setting; the method specifically comprises the following steps:
s1, acquiring vibration signals of a driving motor and a pump motor of an electric forklift in normal states and different fault states through a vibration sensor;
performing fault simulation on common faults of the driving motor and the pump motor through fault measurement equipment to obtain electric signals of current, voltage, rotating speed and torque during normal operation and fault operation of the driving motor and the pump motor;
when the motor fails, its operating state changes and both dynamic and vibration characteristics are affected. In most cases, the vibration signal can be measured by an acceleration sensor.
S2, identifying the running states of a driving motor and a pump motor of the electric forklift through a signal processing method and an intelligent method according to the data of the input vibration signals, and processing the vibration signals into time domain features of the vibration data;
according to the input electric signals, the running states of a driving motor and a pump motor of the electric forklift are identified through a signal processing method and an intelligent method, and vibration signals are preprocessed into time domain features of vibration data;
in the running process of the motor, the motor is interfered by environmental factors and various external influences, and the motor generally runs in a relatively complex circuit system and is influenced by other excitation sources, so that the motor can generate physical deformation, rotation speed change, temperature change and other conditions, thereby generating nonlinear vibration signals. In the signal acquisition process of motor fault diagnosis, the acquired original signals comprise mixed signals of noise and frequency multiplication signals, and effective vibration characteristics need to be extracted by a reasonable method.
S3, carrying out fault simulation on common faults of the driving motor and the pump motor, obtaining data of current, voltage, rotating speed and torque when the driving motor and the pump motor normally run and run in a fault mode, carrying out data processing on vibration signals and electric signals, guiding the vibration signals and the electric signals into a system to carry out fault diagnosis, preprocessing the obtained data through wavelet packet decomposition, extracting fault feature vectors, finally inputting the feature vectors into a BP neural network to carry out training, carrying out identification and judgment on fault types of the driving motor and the pump motor, carrying out data processing on the vibration signals and the electric signals, guiding the vibration signals and the electric signals into the system to carry out fault diagnosis, and therefore realizing fault mode identification of the driving motor and the pump motor.
As shown in fig. 1, the system sets inputtable device parameters for model analysis and user management.
The system settings may give possible causes and treatments for the faults as follows:
winding failure: may be caused by winding breaks, poor contacts, short circuits, etc. The processing method comprises checking and repairing winding connection, replacing winding wires, repairing or replacing windings.
Virtual ground fault: one of the motor windings is grounded in phase, possibly due to aging of the winding insulation, insulation damage or external environment. The treatment method comprises checking the insulation condition of the winding, and repairing or replacing the winding insulation.
Bearing failure: motor bearing wear, overheating or poor lubrication can lead to problems such as noise, vibration, etc. The treatment method comprises the steps of checking the condition of the bearing, repairing or replacing the bearing, and ensuring good lubrication.
Overload failure: the motor can cause problems such as overheating and winding damage after long-time overload operation. The processing method comprises the steps of adjusting the load of the motor and avoiding long-time overload operation.
Vibration and unbalance: possibly due to unbalance or axial errors of the rotating parts of the motor. The processing method comprises the steps of carrying out dynamic balance adjustment to ensure the balance of the rotating parts of the motor.
Power supply problem: motor power supply instability, too high or too low a voltage may lead to motor failure. The processing method comprises the steps of checking power supply and repairing power supply problems.
The driving motor is a power source for the electric forklift to run and is a conversion part between electric energy and mechanical energy, the pump motor drives the plunger to reciprocate up and down in the plunger sleeve, high-pressure oil is generated and supplied to the oil sprayer, and different vibration signals are output in different working states and faults.
Vibration signals and electrical signals of the normal state and the fault simulation state of the driving motor and the pump motor apparatus include:
the system faults are divided into drive motor faults, drive motor controller faults, stator winding faults and bearing faults according to the types of the drive motor faults. And summarizing faults corresponding to each fault type according to the fault mechanism knowledge of the motor system, and forming a fault tree of the driving motor of the electric forklift through a fault tree analysis method.
The failure of the driving motor mainly comprises overhigh temperature of the driving motor, overhigh power of the driving motor, overhigh rotating speed of the driving motor, overhigh torque of the driving motor and lower efficiency of the driving motor; the failure of the driving motor controller mainly comprises overhigh temperature of the motor controller, overlarge input voltage of the motor controller, overlarge current of a direct-current bus of the motor controller and overtemperature of a radiator.
The system faults are divided into interphase short circuit, single-phase grounding, open-circuit faults, demagnetizing faults and IGBT faults according to the fault types of the pump motors.
The fault signal conditioning obtains the characteristic vectors corresponding to the current, the voltage, the torque and the rotating speed under other fault states through wavelet packet decomposition and reconstruction, takes the characteristic vectors as an input sample of the BP neural network, and provides data support for the identification and classification of the neural network fault modes.
As shown in fig. 2, the fault diagnosis and analysis algorithm can be added into a fish swarm algorithm for optimization;
1) Firstly, initializing a fish swarm, wherein the initialization comprises a swarm scale, a fish initial position, a fish foraging speed and the like;
2) According to the objective function, calculating the fitness value, individual extremum, population extremum and the like of each fish in the solution space;
3) Comparing the fitness value with the individual extremum for each fish, and when the fitness value is smaller than the individual extremum, replacing the individual extremum with the fitness value to update the individual extremum; and comparing the fitness value with the population extremum, and when the fitness value is smaller than the population extremum, replacing the population extremum with the fitness value to update the population extremum.
4) Iteratively updating the position and the speed of the fish in the solution space;
5) And (3) calculating the individual extremum and the population extremum of the particles after iterative updating, comparing the individual extremum and the population extremum with a termination condition, outputting an optimized optimal solution if the conditions are met, and repeating the steps (2), (3) and (4) if the conditions are not met until the output conditions are met or the set maximum iteration times are reached.
According to the fault diagnosis method for the double-motor system, provided by the embodiment of the application, the vibration signals and the electric signals are identified, the vibration signals and the electric signals are subjected to data processing and are led into the system to be subjected to fault diagnosis, so that the fault modes of the driving motor and the pump motor are identified. The motor fault can be found and solved in time, further damage is prevented, the service life of the electric forklift is prolonged, and maintenance and replacement costs are reduced.
Embodiment two:
as shown in fig. 3, a second embodiment of the present application provides a dual-motor system fault diagnosis device, which includes a data hardware acquisition module, a fault signal conditioning module, a fault diagnosis analysis module and a system setting module;
the data hardware acquisition module is mainly used for acquiring data of different faults of the motor;
the fault signal conditioning module displays the acquired vibration signals to a data acquisition interface through a data acquisition industrial personal computer, a data acquisition card and LabVIEW software, and displays the vibration signals to a user in a graphical mode, wherein the vibration signals comprise a time domain waveform diagram, a spectrogram and the like;
the fault diagnosis analysis module can process the vibration signals and guide the vibration signals into the system to perform fault diagnosis, and the displayed results represent the running states of the driving motor and the pump motor of the corresponding electric fork truck, so that the whole fault diagnosis process of the fault motor is completed.
The system setup module may input device parameters for model analysis and user management.
Embodiment III:
the third embodiment of the application provides an electric forklift, which is used for the fault diagnosis method and the fault diagnosis device of the double-motor system in any one of the first embodiment and the second embodiment.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
Claims (9)
1. A method for diagnosing faults of a dual motor system, comprising the steps of:
s1, data hardware acquisition;
s11, vibration signals of the driving motor and the pump motor in normal states and different fault states are acquired through the vibration sensor;
s12, performing fault simulation on common faults of the driving motor and the pump motor through fault measurement equipment to obtain electric signals of current, voltage, rotating speed and torque during normal operation and fault operation of the driving motor and the pump motor;
s2, conditioning the currently acquired original fault signals;
s21, according to the data of the input vibration signals, the operation states of the driving motor and the pump motor are identified through a signal processing method and an intelligent method, and the vibration signals are processed into time domain features of the vibration data;
s22, preprocessing a vibration signal into time domain characteristics of vibration data according to the running states of the driving motor and the pump motor which are identified by the signal processing method and the intelligent method;
s3, fault diagnosis and analysis are carried out;
s31, performing fault simulation on common faults of the driving motor and the pump motor, and acquiring data of current, voltage, rotating speed and torque during normal operation and fault operation of the driving motor and the pump motor;
s32, carrying out data processing on the vibration signals and the electric signals, importing the vibration signals and the electric signals into a system for fault diagnosis, preprocessing the acquired data through wavelet packet decomposition, extracting fault feature vectors, finally inputting the feature vectors into a BP neural network for training, providing data support for the identification and classification of the fault modes of the neural network, and carrying out the identification and judgment of the fault types of the driving motor and the pump motor;
s33, processing data of vibration signals and electric signals acquired when faults occur, and importing the vibration signals and the electric signals into a system to perform fault diagnosis, so that fault mode identification of the driving motor and the pump motor is realized.
2. The dual motor system fault diagnosis method according to claim 1, wherein the vibration signals and electrical signals of the normal state and the fault simulation state of the driving motor and the pump motor apparatus include:
dividing system faults into drive motor faults, drive motor controller faults, stator winding faults and bearing faults according to the types of the drive motor faults;
the system faults are divided into interphase short circuit, single-phase grounding, open-circuit faults, demagnetizing faults and IGBT faults according to the fault types of the pump motors.
3. The dual motor system fault diagnosis method according to claim 1, characterized in that: the fault signal conditioning obtains the characteristic vectors corresponding to the current, the voltage, the torque and the rotating speed under other fault states through wavelet packet decomposition and reconstruction, takes the characteristic vectors as an input sample of the BP neural network, and provides data support for the identification and the classification of the neural network fault modes.
4. The dual motor system fault diagnosis method according to claim 1, wherein the fault diagnosis analysis algorithm comprises an optimized shoal of fish algorithm, the optimized shoal of fish algorithm comprising the steps of:
step one, initializing a fish swarm, wherein the fish swarm comprises a population scale, a fish initial position, a fish foraging speed and the like;
step two, calculating the fitness value, the individual extremum, the population extremum and the like of each fish in the solution space according to the objective function;
step three, comparing the fitness value with the individual extremum for each fish, and when the fitness value is smaller than the individual extremum, replacing the individual extremum with the fitness value to update the individual extremum; comparing the fitness value with the population extremum, and when the fitness value is less than the population extremum, replacing the population extremum with the fitness value to update the population extremum;
step four, iteratively updating the position and the speed of the fish in the solution space;
and fifthly, calculating individual extremum and population extremum of the particles after iterative updating, comparing the individual extremum and population extremum with a termination condition, outputting an optimized optimal solution if the conditions are met, and repeating the steps of the steps II, III and IV if the conditions are not met until the output conditions are met or the set maximum iteration times are reached.
5. The dual motor system fault diagnosis method according to claim 1, characterized in that: the system setting comprises possible fault reasons and processing methods as follows;
winding faults, which may be caused by winding wire breaks, poor contacts, short circuits, etc.; the processing method comprises checking and repairing winding connection, replacing winding wires, repairing or replacing windings;
virtual ground faults, one phase of the motor windings being grounded, may be due to aging of winding insulation, insulation damage or external environment; the processing method comprises checking the insulation condition of the winding, repairing or replacing the winding insulation;
bearing faults, and problems such as noise, vibration and the like can be caused by abrasion, overheating or poor lubrication of a motor bearing; the processing method comprises the steps of checking the condition of the bearing, repairing or replacing the bearing, and ensuring good lubrication;
overload faults, overheat and damage of windings can be caused by long-time overload operation of the motor; the processing method comprises the steps of adjusting the load of the motor, and avoiding long-time overload operation;
vibration and imbalance, which may be due to motor rotating part imbalance or axial errors; the processing method comprises the steps of performing dynamic balance adjustment to ensure balance of rotating parts of the motor;
power supply problems, unstable power supply of the motor, and excessive high or low voltage can cause motor faults; the processing method comprises the steps of checking power supply and repairing power supply problems.
6. An apparatus, characterized in that the two-motor system fault diagnosis method according to any one of claims 1 to 5 is applied.
7. The diagnostic device of claim 6, wherein: the system comprises a data hardware acquisition module, a fault signal conditioning module, a fault diagnosis analysis module and a system setting module;
the data hardware acquisition module is used for acquiring data of different faults of the motor;
the fault signal conditioning module displays the acquired vibration signals to a data acquisition interface through a data acquisition industrial personal computer, a data acquisition card and LabVIEW software, and displays the vibration signals to a user in a graphical mode, wherein the vibration signals include but are not limited to time domain waveform diagrams and spectrograms;
the fault diagnosis analysis module can process the vibration signals and guide the vibration signals into the system to perform fault diagnosis, and the displayed results represent the running states of the driving motor and the pump motor of the corresponding electric fork truck, so that the whole fault diagnosis process of the fault motor is completed.
8. An electric forklift, characterized in that: use of the dual motor system fault diagnosis method of any one of claims 1-5.
9. An electric forklift, characterized in that: having a diagnostic device according to any one of claims 6-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311236175.8A CN117068190A (en) | 2023-09-25 | 2023-09-25 | Dual-motor system fault diagnosis method and device and electric forklift |
Applications Claiming Priority (1)
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| CN117310496B (en) * | 2023-11-30 | 2024-02-06 | 杭州智仝科技有限公司 | Motor fault diagnosis method for distributed electric drive system |
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